{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,10]],"date-time":"2026-04-10T02:10:11Z","timestamp":1775787011039,"version":"3.50.1"},"reference-count":45,"publisher":"MDPI AG","issue":"10","license":[{"start":{"date-parts":[[2021,9,30]],"date-time":"2021-09-30T00:00:00Z","timestamp":1632960000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Future Internet"],"abstract":"<jats:p>COVID-19 has had broad disruptive effects on economies, healthcare systems, governments, societies, and individuals. Uncertainty concerning the scale of this crisis has given rise to countless rumors, hoaxes, and misinformation. Much of this type of conversation and misinformation about the pandemic now occurs online and in particular on social media platforms like Twitter. This study analysis incorporated a data-driven approach to map the contours of misinformation and contextualize the COVID-19 pandemic with regards to socio-religious-political information. This work consists of a combined system bridging quantitative and qualitative methodologies to assess how information-exchanging behaviors can be used to minimize the effects of emergent misinformation. The study revealed that the social media platforms detected the most significant source of rumors in transmitting information rapidly in the community. It showed that WhatsApp users made up about 46% of the source of rumors in online platforms, while, through Twitter, it demonstrated a declining trend of rumors by 41%. Moreover, the results indicate the second-most common type of misinformation was provided by pharmaceutical companies; however, a prevalent type of misinformation spreading in the world during this pandemic has to do with the biological war. In this combined retrospective analysis of the study, social media with varying approaches in public discourse contributes to efficient public health responses.<\/jats:p>","DOI":"10.3390\/fi13100254","type":"journal-article","created":{"date-parts":[[2021,9,30]],"date-time":"2021-09-30T10:22:42Z","timestamp":1632997362000},"page":"254","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":17,"title":["A Retrospective Analysis of the COVID-19 Infodemic in Saudi Arabia"],"prefix":"10.3390","volume":"13","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-2486-5979","authenticated-orcid":false,"given":"Ashwag","family":"Alasmari","sequence":"first","affiliation":[{"name":"Computer Science Department, King Khalid University, Abha 62529, Saudi Arabia"}]},{"given":"Aseel","family":"Addawood","sequence":"additional","affiliation":[{"name":"Information System Department, Imam Mohammad Bin Saud University, Riyadh 11564, Saudi Arabia"}]},{"given":"Mariam","family":"Nouh","sequence":"additional","affiliation":[{"name":"Center for Complex Engineering Systems (CCES) at KACST and MIT, King Abdulaziz City for Science and Technology, Riyadh 12354, Saudi Arabia"}]},{"given":"Wajanat","family":"Rayes","sequence":"additional","affiliation":[{"name":"Department of Information Science, Umm Al-Qura University, Makkah 21955, Saudi Arabia"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3756-5721","authenticated-orcid":false,"given":"Areej","family":"Al-Wabil","sequence":"additional","affiliation":[{"name":"College of Engineering, Alfaisal University, Riyadh 11533, Saudi Arabia"}]}],"member":"1968","published-online":{"date-parts":[[2021,9,30]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"249","DOI":"10.1145\/3432948","article-title":"Misinformation as a Window into Prejudice: COVID-19 and the Information Environment in India","volume":"4","author":"Akbar","year":"2021","journal-title":"Proc. ACM Hum.-Comput. Interact."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"1285","DOI":"10.1038\/s41562-020-00994-6","article-title":"Assessing the risks of \u2018infodemics\u2019 in response to COVID-19 epidemics","volume":"4","author":"Gallotti","year":"2020","journal-title":"Nat. Hum. Behav."},{"key":"ref_3","unstructured":"Gmi_blogger (2021, September 27). Saudi Arabia Social Media Statistics 2020 (Infographics)\u2014GMI Blog. Available online: https:\/\/froggyads.com\/blog\/saudi-arabia-social-media-statistics-infographics-gmi-blog\/."},{"key":"ref_4","unstructured":"Al-Masoudi, M. (2021, September 27). Twitter in Saudi Arabia. Available online: https:\/\/twitter.com\/saudiarabia."},{"key":"ref_5","unstructured":"Alamro, N., Almana, L., Alabduljabbar, A., AlKahtani, M., AlDihan, R., Almansour, A., Alobaid, N., AlOthaim, N., and Alshunaifi, A. (2021, September 27). Saudi Arabia COVID-19 Snapshot MOnitoring (COSMO Saudi): Monitoring Knowledge, Risk Perceptions, Preventive Behaviours, and Public Trust in the Current Coronavirus Outbreak in Saudi Arabia. PsychArchives, Available online: https:\/\/www.psycharchives.org\/handle\/20.500.12034\/2496."},{"key":"ref_6","unstructured":"Alharbi, A., and Lee, M. (2021, January 19\u201323). Kawarith: An Arabic Twitter Corpus for Crisis Events. Proceedings of the Sixth Arabic Natural Language Processing Workshop, online."},{"key":"ref_7","unstructured":"Alqurashi, S., Alhindi, A., and Alanazi, E. (2020). Large Arabic Twitter Dataset on COVID-19. arXiv."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"e22609","DOI":"10.2196\/22609","article-title":"Detection of Hate Speech in COVID-19\u2014Related Tweets in the Arab Region: Deep Learning and Topic Modeling Approach","volume":"22","author":"Alshalan","year":"2020","journal-title":"J. Med. Internet Res."},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Alsudias, L., and Rayson, P. (2020, January 9). COVID-19 and Arabic Twitter: How can Arab World Governments and Public Health Organizations Learn from Social Media?. Proceedings of the NLP COVID-19 Workshop, Seattle, WA, USA.","DOI":"10.2196\/27670"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"g6178","DOI":"10.1136\/bmj.g6178","article-title":"Ebola, Twitter, and misinformation: A dangerous combination?","volume":"349","author":"Oyeyemi","year":"2014","journal-title":"BMJ"},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Chew, C., and Eysenbach, G. (2010). Pandemics in the Age of Twitter: Content Analysis of Tweets during the 2009 H1N1 Outbreak. PLoS ONE, 5.","DOI":"10.1371\/journal.pone.0014118"},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Badawy, A., Ferrara, E., and Lerman, K. (2018, January 28\u201331). Analyzing the Digital Traces of Political Manipulation: The 2016 Russian Interference Twitter Campaign. Proceedings of the 2018 IEEE\/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM), Barcelona, Spain.","DOI":"10.1109\/ASONAM.2018.8508646"},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Ferrara, E. (2015). Manipulation and abuse on social media. ACM SIGWEB Newsl., 1\u20139.","DOI":"10.1145\/2749279.2749283"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"taaa031","DOI":"10.1093\/jtm\/taaa031","article-title":"The pandemic of social media panic travels faster than the COVID-19 outbreak","volume":"27","author":"Depoux","year":"2020","journal-title":"J. Travel Med."},{"key":"ref_15","unstructured":"(2021, September 27). SR1m Fine and 5 Years Jail for Violating Coronavirus Measures\u2014Saudi Gazette. Available online: https:\/\/saudigazette.com.sa\/article\/592724\/SAUDI-ARABIA\/SR1m-fine-and-5-years-jail-for-violating-coronavirus-measures."},{"key":"ref_16","unstructured":"Haouari, F., Hasanain, M., Suwaileh, R., and Elsayed, T. (2021). ArCOV19-Rumors: Arabic COVID-19 Twitter Dataset for Misinformation Detection. arXiv."},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Jussila, J., Suominen, A., Partanen, A., and Honkanen, T. (2021). Text analysis methods for misinformation-related research on Finnish language twitter. Future Internet, 13.","DOI":"10.3390\/fi13060157"},{"key":"ref_18","unstructured":"Alqurashi, S., Hamoui, B., Alashaikh, A., Alhindi, A., and Alanazi, E. (2021). Eating Garlic Prevents COVID-19 Infection: Detecting Misinformation on the Arabic Content of Twitter. arXiv."},{"key":"ref_19","unstructured":"WHO (2021). 4th Virtual WHO Infodemic Management Conference: Advances in Social Listening for Public Health, WHO."},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Adel, G., and Wang, Y. (2019, January 20\u201322). Arabic Twitter Corpus for Crisis Response Messages Classification. Proceedings of the 2019 2nd International Conference on Algorithms, Computing and Artificial Intelligence, Sanya, China.","DOI":"10.1145\/3377713.3377799"},{"key":"ref_21","unstructured":"Haouari, F., Hasanain, M., Suwaileh, R., and Elsayed, T. (2021, January 19\u201323). ArCOV-19: The First Arabic COVID-19 Twitter Dataset with Propagation Networks. Proceedings of the Sixth Arabic Natural Language Processing Workshop, online."},{"key":"ref_22","unstructured":"Mubarak, H., and Hassan, S. (2021). ArCorona: Analyzing Arabic Tweets in the Early Days of Coronavirus (COVID-19) Pandemic. arXiv."},{"key":"ref_23","unstructured":"Addawood, A. (2021, September 27). Coronavirus: Public Arabic Twitter Data Set. Available online: https:\/\/www.preprints.org\/manuscript\/202004.0263\/v1."},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Alam, F., Shaar, S., Dalvi, F., Sajjad, H., Nikolov, A., Mubarak, H., Martino, G.D.S., Abdelali, A., Durrani, N., and Darwish, K. (2020). Fighting the COVID-19 Infodemic: Modeling the Perspective of Journalists, Fact-Checkers, Social Media Platforms, Policy Makers, and the Society. arXiv.","DOI":"10.18653\/v1\/2021.findings-emnlp.56"},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"e19659","DOI":"10.2196\/19659","article-title":"Framework for managing the COVID-19 infodemic: Methods and results of an online, crowdsourced WHO technical consultation","volume":"22","author":"Tangcharoensathien","year":"2020","journal-title":"J. Med. Internet Res."},{"key":"ref_26","unstructured":"UNESCO (2020). Combating the Disinfodemic: Working for Truth in the Time of COVID-19, United Nations Educational, Scientific and Cultural Organization."},{"key":"ref_27","unstructured":"News, U. (2020). During this Coronavirus Pandemic, \u2018Fake News\u2019 is Putting Lives at Risk, UNESCO."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"62","DOI":"10.1111\/j.1471-1842.2007.00704.x","article-title":"International perspectives and initiatives","volume":"24","author":"Murphy","year":"2007","journal-title":"Health Inf. Libr. J."},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Chen, E., Jiang, J., Chang, H.-C.H., Muric, G., and Ferrara, E. (2021). COVID-19 Infodemiology at Planetary Scale: Charting the Information and Misinformation Landscape to Characterize Misinfodemics Spread on Social Media. JMIR Prepr.","DOI":"10.2196\/32378"},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Vargas, L., Emami, P., and Traynor, P. (2020, January 9). On the Detection of Disinformation Campaign Activity with Network Analysis. Proceedings of the 2020 ACM SIGSAC Conference on Cloud Computing Security Workshop, New York, NY, USA.","DOI":"10.1145\/3411495.3421363"},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"21","DOI":"10.25300\/MISQ\/2013\/37.1.02","article-title":"Bridging the qualitative-quantitative divide: Guidelines for conducting mixed methods research in information systems","volume":"37","author":"Venkatesh","year":"2013","journal-title":"MIS Q."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"141","DOI":"10.1177\/004912418101000205","article-title":"Snowball sampling: Problems and techniques of chain referral sampling","volume":"10","author":"Biernacki","year":"1981","journal-title":"Sociol. Methods Res."},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Al-Zaman, M.S. (2021). A Thematic Analysis of Misinformation in India during the COVID-19 Pandemic. Int. Inf. Libr. Rev.","DOI":"10.1080\/10572317.2021.1908063"},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"e21642","DOI":"10.2196\/21642","article-title":"Share to Seek: The Effects of Disease Complexity on Health Information Seeking Behavior","volume":"23","author":"Alasmari","year":"2021","journal-title":"J. Med. Internet Res."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"103958","DOI":"10.1016\/j.ijmedinf.2019.103958","article-title":"How multimorbid health information consumers interact in an online community Q&A platform","volume":"131","author":"Alasmari","year":"2019","journal-title":"Int. J. Med. Inform."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"45","DOI":"10.3389\/fcomm.2020.00045","article-title":"Rising Above Misinformation or Fake News in Africa: Another Strategy to Control COVID-19 Spread","volume":"5","author":"Ahinkorah","year":"2020","journal-title":"Front. Commun."},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Adly, H., Aljahdali, I., Garout, M., Khafagy, A., Saati, A., and Saleh, S. (2020). Correlation of COVID-19 Pandemic with Healthcare System Response and Prevention Measures in Saudi Arabia. Int. J. Environ. Res. Public Health, 17.","DOI":"10.3390\/ijerph17186666"},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"e19338","DOI":"10.2196\/19338","article-title":"Digital Response During the COVID-19 Pandemic in Saudi Arabia","volume":"22","author":"Hassounah","year":"2020","journal-title":"J. Med. Internet Res."},{"key":"ref_39","unstructured":"(2021, September 27). Saudis Fight Misinformation Related to Coronavirus Disease|Arab News. Available online: https:\/\/www.arabnews.com\/node\/1662936\/saudi-arabia."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"881","DOI":"10.1007\/s10900-020-00827-7","article-title":"Knowledge, Perceptions, and Attitude of Egyptians Towards the Novel Coronavirus Disease (COVID-19)","volume":"45","author":"Mohammed","year":"2020","journal-title":"J. Community Health"},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"100","DOI":"10.7326\/M20-1239","article-title":"Awareness, Attitudes, and Actions Related to COVID-19 Among Adults With Chronic Conditions at the Onset of the U.S. Outbreak","volume":"173","author":"Wolf","year":"2020","journal-title":"Ann. Intern. Med."},{"key":"ref_42","first-page":"100006","article-title":"What Is Required to Prevent a Second Major Outbreak of SARS-CoV-2 upon Lifting Quarantine in Wuhan City, China","volume":"1","author":"Zhang","year":"2020","journal-title":"Innovation"},{"key":"ref_43","doi-asserted-by":"crossref","unstructured":"Nouh, M., Nurse, J.R., and Goldsmith, M. (2019, January 1\u20133). Understanding the Radical Mind: Identifying Signals to Detect Extremist Content on Twitter. Proceedings of the 2019 IEEE International Conference on Intelligence and Security Informatics (ISI), Shenzhen, China.","DOI":"10.1109\/ISI.2019.8823548"},{"key":"ref_44","doi-asserted-by":"crossref","unstructured":"Al-Twairesh, N., Al-Khalifa, H., and Al-Salman, A. (2015, January 17\u201320). Towards analyzing Saudi tweets. Proceedings of the 2015 First International Conference on Arabic Computational Linguistics (ACLing), Cairo, Egypt.","DOI":"10.1109\/ACLing.2015.23"},{"key":"ref_45","unstructured":"WHO (2020). Report of the WHO-China Joint Mission on Coronavirus Disease 2019 (COVID-19), WHO."}],"container-title":["Future Internet"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1999-5903\/13\/10\/254\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T07:07:53Z","timestamp":1760166473000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1999-5903\/13\/10\/254"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,9,30]]},"references-count":45,"journal-issue":{"issue":"10","published-online":{"date-parts":[[2021,10]]}},"alternative-id":["fi13100254"],"URL":"https:\/\/doi.org\/10.3390\/fi13100254","relation":{},"ISSN":["1999-5903"],"issn-type":[{"value":"1999-5903","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,9,30]]}}}